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‎.travis.yml‎

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@@ -5,25 +5,26 @@ cache:
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- $HOME/.m2
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- $HOME/.cache/pip
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- $HOME/conda
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# Verify step requires pandas and protobuf Python packages
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install: mvn package -B
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matrix:
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include:
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- os: linux
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jdk: oraclejdk8
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script: xvfb-run mvn verify -B
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env:
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- INSTALLER=Miniconda-latest-Linux-x86_64.sh
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before_install:
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- if [ ! -e $HOME/conda/bin/activate ]; then
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wget -nc https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh;
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bash Miniconda3-latest-Linux-x86_64.sh -f -b -p $HOME/conda;
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fi
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- os: osx
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osx_image: xcode8.2
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osx_image: xcode8.3
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script: Xvfb :5009 & export DISPLAY=:5009 && mvn verify -B
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env:
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- INSTALLER=Miniconda2-latest-MacOSX-x86_64.sh
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before_install:
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- if [ "$TRAVIS_OS_NAME" == "osx" ]; then export JAVA_HOME=$(/usr/libexec/java_home); fi
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- if [ ! -e $HOME/conda/bin/activate ]; then
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wget -nc https://repo.continuum.io/miniconda/$INSTALLER;
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bash $INSTALLER -f -b -p $HOME/conda;
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fi
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- source $HOME/conda/bin/activate root
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- conda install -y -q pandas protobuf
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before_install:
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- if [ ! -e $HOME/conda/bin/activate ]; then
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wget -nc https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh;
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bash Miniconda3-latest-MacOSX-x86_64.sh -f -b -p $HOME/conda;
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fi
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- export JAVA_HOME=$(/usr/libexec/java_home)
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install:
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- source $HOME/conda/bin/activate root
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- conda install -y -q pandas protobuf
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- mvn package -B

‎README.md‎

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# Knime Python node archetype
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# KNIME Python node archetype
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[![Build Status](https://travis-ci.org/3D-e-Chem/knime-python-node-archetype.svg?branch=master)](https://travis-ci.org/3D-e-Chem/knime-python-node-archetype)
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[![Build status](https://ci.appveyor.com/api/projects/status/5dory9qjycepcmcn/branch/master?svg=true)](https://ci.appveyor.com/project/3D-e-Chem/knime-python-node-archetype/branch/master)
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[![Download](https://api.bintray.com/packages/nlesc/knime-python-node-archetype/knime-python-node-archetype/images/download.svg) ](https://bintray.com/nlesc/knime-python-node-archetype/knime-python-node-archetype/_latestVersion)
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Generates [Knime](http://www.knime.org) workflow node skeleton repository with sample code.
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Generates [KNIME](http://www.knime.org) workflow node skeleton repository with sample code.
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The node executes a Python script which is included in the skeleton.
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The script uses dictionary for dialog options and [Pandas](http://pandas.pydata.org/) DataFrames as input and output.
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This archetype was made because the instructions to create Knime nodes at https://tech.knime.org/developer-guide, requires interaction with Eclipse wizards. We wanted a way to start and perform node development from the command line and headless.
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Knime nodes are Eclipse plugins. The [Tycho](https://eclipse.org/tycho/) Maven plugin is used to build and handle dependencies of Eclipse plugins, so we use Tycho for Knime node building.
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This archetype was made because the instructions to create KNIME nodes at https://tech.knime.org/developer-guide, requires interaction with Eclipse wizards. We wanted a way to start and perform node development from the command line and headless.
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KNIME nodes are Eclipse plugins. The [Tycho](https://eclipse.org/tycho/) Maven plugin is used to build and handle dependencies of Eclipse plugins, so we use Tycho for KNIME node building.
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The [Maven archetype](https://maven.apache.org/guides/introduction/introduction-to-archetypes.html) will generate a multi-module project with the following structure:
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* / - parent Maven project
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* /plugin/ - code for Knime node
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* /tests/ - tests of Knime node
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* /plugin/ - code for KNIME node
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* /tests/ - tests of KNIME node
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* /feature/ - eclipse feature
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* /p2/ - eclipse update site
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The Knime node will execute a Python script called `/plugin/src/<package>/<python script file name>.py`.
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The KNIME node will execute a Python script called `/plugin/src/<package>/<python script file name>.py`.
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See https://github.com/3D-e-Chem/knime-python-wrapper for more information how the Python Wrapper node works.
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* Java >=1.8
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* Maven >=3.0
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The archetype is hosted on a BinTray repository. Maven does not resolve to this BinTray repository by default so it must be added.
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The ~/.m2/settings.xml should contain the following profile:
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```xml
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<?xml version="1.0" encoding="UTF-8" ?>
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<settings xmlns="http://maven.apache.org/SETTINGS/1.0.0"
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xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
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xsi:schemaLocation="http://maven.apache.org/SETTINGS/1.0.0
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https://maven.apache.org/xsd/settings-1.0.0.xsd">
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<profiles>
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<profile>
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<id>knimearchetype</id>
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<repositories>
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<repository>
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<id>python-knime-archetype</id>
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<url>https://dl.bintray.com/nlesc/knime-python-node-archetype</url>
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</repository>
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</repositories>
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</profile>
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</profiles>
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</settings>
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```
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## Generate
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1. Execute
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```
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The following command will generate a skeleton project
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```sh
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mvn archetype:generate -DarchetypeGroupId=nl.esciencecenter \
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-DarchetypeArtifactId=knime-python-node-archetype \
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-DarchetypeVersion=1.3.0 \
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-DarchetypeRepository=https://dl.bintray.com/nlesc/knime-python-node-archetype
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-DarchetypeArtifactId=knime-python-node-archetype \
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-DarchetypeVersion=1.3.0 \
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-P knimearchetype
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```
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2. Enter the groupId
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3. Enter the artifactId
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4. Enter the name of the package under which your code will be created
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5. Enter the version of your project
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6. Enter the Github organization name or Github username
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7. Enter the Github repository name
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8. Enter the Knime node name
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9. Enter the Python script file name (must be given without .py extension)
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10. Enter the required Python package name
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11. Confirm
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12. Change directory to generated code.
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The command will ask the following questions:
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1. Enter the groupId
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2. Enter the artifactId
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3. Enter the name of the package under which your code will be created
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4. Enter the version of your project, use `x.y.z-SNAPSHOT` format (for example `1.2.3-SNAPSHOT`), where x.y.z is [semantic versioning](http://semver.org/).
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5. Enter the GitHub organization name or GitHub username
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6. Enter the GitHub repository name
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7. Enter the KNIME node name
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8. Enter the Python script file name (must be given without .py extension)
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9. Enter the required Python package name (The presence of this Python package will be checked before executing the node)
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10. Confirm
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The skeleton has been generated in a sub-directory named after the artifactId in the current working directory.
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The following steps are needed to get a ready to use project.
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11. Change directory to generated code.
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12. Make skeleton git aware, by running `git init`.
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13. Fill in all placeholders (`[Enter ... here.]`) in
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* plugin/META-INF/MANIFEST.MF
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* plugin/src/**/*.xml
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* feature/feature.xml
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* p2/category.xml
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14. Commit all changes and push to GitHub
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15. Optionally, setup Continuous Integration as described in the project README.md file.
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Further instructions about generated project can be found in it's README.md file.
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‎appveyor.yml‎

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cache: C:\Users\appveyor\.m2
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environment:
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matrix:
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- PYTHON_VERSION: 2.7
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MINICONDA: C:\Miniconda-x64
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- PYTHON_VERSION: 3.6
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MINICONDA: C:\Miniconda3-x64
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init:
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- mvn --version
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- "ECHO %PYTHON_VERSION% %MINICONDA%"
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install:
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- "set PATH=%MINICONDA%;%MINICONDA%\\Scripts;%PATH%"
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- conda install -y -q -c conda-forge pandas protobuf
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# KNIME defaults to python3 as executable, but conda only has python.exe, create python3 so tests can use it
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- copy /Y %MINICONDA%\python.exe %MINICONDA%\python3.exe
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build_script:
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- cmd: mvn package -B
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test_script:

‎pom.xml‎

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<groupId>nl.esciencecenter</groupId>
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<artifactId>knime-python-node-archetype</artifactId>
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<version>1.3.0</version>
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<name>Archetype - Knime Python node</name>
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<name>Archetype - KNIME Python node</name>
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<packaging>maven-archetype</packaging>
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<licenses>
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<license>
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<tagNameFormat>v@{project.version}</tagNameFormat>
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</configuration>
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</plugin>
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<plugin>
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<groupId>org.apache.maven.plugins</groupId>
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<artifactId>maven-resources-plugin</artifactId>
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<version>3.0.2</version>
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<configuration>
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<!-- Required so that .gitignore gets included in archetypes; see https://issues.apache.org/jira/browse/MRESOURCES-190 -->
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<addDefaultExcludes>false</addDefaultExcludes>
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</configuration>
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</plugin>
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</plugins>
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</pluginManagement>
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</build>

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